Sequence features accurately predict genome-wide MeCP2 binding in vivo

H. Tomas Rube, Wooje Lee, Miroslav Hejna, Huaiyang Chen, Dag H. Yasui, John F. Hess, Janine M. LaSalle, Jun S. Song () and Qizhi Gong ()
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H. Tomas Rube: Carl R. Woese Institute for Genomic Biology, University of Illinois
Wooje Lee: University of California School of Medicine
Miroslav Hejna: Carl R. Woese Institute for Genomic Biology, University of Illinois
Huaiyang Chen: University of California School of Medicine
Dag H. Yasui: Genome Center, MIND Institute, University of California School of Medicine
John F. Hess: University of California School of Medicine
Janine M. LaSalle: Genome Center, MIND Institute, University of California School of Medicine
Jun S. Song: Carl R. Woese Institute for Genomic Biology, University of Illinois
Qizhi Gong: University of California School of Medicine

Nature Communications, 2016, vol. 7, issue 1, 1-12

Abstract: Abstract Methyl-CpG binding protein 2 (MeCP2) is critical for proper brain development and expressed at near-histone levels in neurons, but the mechanism of its genomic localization remains poorly understood. Using high-resolution MeCP2-binding data, we show that DNA sequence features alone can predict binding with 88% accuracy. Integrating MeCP2 binding and DNA methylation in a probabilistic graphical model, we demonstrate that previously reported genome-wide association with methylation is in part due to MeCP2’s affinity to GC-rich chromatin, a result replicated using published data. Furthermore, MeCP2 co-localizes with nucleosomes. Finally, MeCP2 binding downstream of promoters correlates with increased expression in Mecp2-deficient neurons.

Date: 2016
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DOI: 10.1038/ncomms11025

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